ShapeAXI: Shape Analysis Explainability and Interpretability.

ShapeAXI represents a cutting-edge framework for shape analysis that leverages a multi-view approach, capturing 3D objects from diverse viewpoints and subsequently analyzing them via 2D Convolutional Neural Networks (CNNs). We implement an automatic N-fold cross-validation process and aggregate the results across all folds. This ensures insightful explainability heat-maps for each class across every shape, enhancing interpretability and contributing to a more nuanced understanding of the underlying phenomena. We demonstrate the versatility of ShapeAXI through two targeted classification experiments. The first experiment categorizes condyles into healthy and degenerative states. The second, more intricate experiment, engages with shapes extracted from CBCT scans of cleft patients, efficiently classifying them into four severity classes. This innovative application not only aligns with existing medical research but also opens new avenues for specialized cleft patient analysis, holding considerable promise for both scientific exploration and clinical practice. The rich insights derived from ShapeAXI's explainability images reinforce existing knowledge and provide a platform for fresh discovery in the fields of condyle assessment and cleft patient severity classification. As a versatile and interpretative tool, ShapeAXI sets a new benchmark in 3D object interpretation and classification, and its groundbreaking approach hopes to make significant contributions to research and practical applications across various domains. ShapeAXI is available in our GitHub repository https://github.com/DCBIA-OrthoLab/ShapeAXI.

Artificial intelligence as a prediction tool for orthognathic surgery assessment.

INTRODUCTION: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. METHODS: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). RESULTS: The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). CONCLUSIONS: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.

Is three-piece maxillary segmentation surgery a stable procedure?

Objective: : The number of three-piece maxillary osteotomies has increased over the years; however, the literature remains controversial. The objective of this study was to evaluate the skeletal stability of this surgical modality compared with that of one-piece maxillary osteotomy. Methods: : This retrospective cohort study included 39 individuals who underwent Le Fort I maxillary osteotomies and were divided into two groups: group 1 (three pieces, n = 22) and group 2 (one piece, n = 17). Three cone-beam computed tomography scans from each patient (T1, pre-surgical; T2, post-surgical; and T3, follow-up) were used to evaluate the three-dimensional skeletal changes. Results: : The differences within groups were statistically significant only for group 1 in terms of surgical changes (T2-T1) with a mean difference in the canine region of 3.09 mm and the posterior region of 3.08 mm. No significant differences in surgical stability were identified between or within the groups. The mean values of the differences between groups were 0.05 mm (posterior region) and -0.39 mm (canine region). Conclusions: : Our findings suggest that one- and three-piece maxillary osteotomies result in similar post-surgical skeletal stability.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Enhancing skeletal stability and Class III correction through active orthodontist engagement in virtual surgical planning: A voxel-based 3-dimensional analysis.

INTRODUCTION: Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes. METHODS: A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24.8 ± 3.5 years). Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of α = 0.05. RESULTS: The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1.84 ± 1.50 mm; P = 0.004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1.01 ± 3.66 mm; T1-T2, 0.32 ± 4.17 mm) and angular (T1-VSP, 1.53° ± 1.60°; T1-T2, 1.54° ± 1.50°) displacements (P >0.05). The proximal segments exhibited lateral displacement within 1.5° for both the mandibular right and left ramus at T1-VSP and T1-T2 (P >0.05). CONCLUSIONS: The analysis of fully digital planned and surgically repositioned maxilla and mandible revealed excellent precision. In the midterm surgical outcomes of maxillary advancement, a minor deviation from the planned anterior movement was observed.

Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.

OBJECTIVE(S): This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools. MATERIALS AND METHODS: Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared. RESULTS: The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth. CONCLUSION: The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.

Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate.

Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.

Clear aligner mandibular advancement in growing patients with Class II malocclusion.

Treatment effects occurring during Class II malocclusion treatment with the clear aligner mandibular advancement protocol were evaluated in two growing patients: one male (12 years, 3 months) and one female (11 years, 9 months). Both patients presented with full cusp Class II molar and canine relationships. Intraoral scans and cone-beam computed tomography were acquired before treatment and after mandibular advancement. Three-dimensional skeletal and dental long-axis changes were quantified, in which the dental long axis was determined by registering the dental crowns obtained from intraoral scans to the root canals in cone-beam computed tomography scans obtained at the same time points. Class II correction was achieved by a combination of mandibular skeletal and dental changes. A similar direction of skeletal and dental changes was observed in both patients, with downward and forward displacement of the mandible resulting from the growth of the mandibular condyle and ramus. Dental changes in both patients included mesialization of the mandibular posterior teeth with flaring of mandibular anterior teeth. In these two patients, clear aligner mandibular advancement was an effective treatment modality for Class II malocclusion correction with skeletal and dental effects and facial profile improvement.

Three-dimensional comparison between the effects of mandibular advancement device and maxillomandibular advancement surgery on upper airway.

BACKGROUND: The efficacy of mandibular advancement devices (MAD) and maxillomandibular advancement (MMA) in improving upper airway (UA) patency has been described as being comparable to continuous positive airway pressure (CPAP) outcomes. However, no previous study has compared MAD and MMA treatment outcomes for the upper airway enlargement. This study aimed to evaluate three-dimensionally the UA changes and mandibular rotation in patients after MAD compared to MMA. METHODS: The sample consisted of 17 patients with treated with MAD and 17 patients treated with MMA matched by weight, height, body mass index. Cone-beam computed tomography from before and after both treatments were used to measure total UA, superior/inferior oropharynx volume and surface area; and mandibular rotation. RESULTS: Both groups showed a significant increase in the superior oropharynx volume after the treatments (p = 0.003) and the MMA group showed greater increase (p = 0.010). No statistical difference was identified in the MAD group considering the inferior volume, while the MMA group showed a significantly gain (p = 0.010) and greater volume (p = 0.024). Both groups showed anterior mandibular displacement. However, the mandibular rotation were statistically different between the groups (p < 0.001). While the MAD group showed a clockwise rotation pattern (-3.97 ± 1.07 and - 4.08 ± 1.30), the MMA group demonstrated a counterclockwise (2.40 ± 3.43 and 3.41 ± 2.79). In the MAD group, the mandibular linear anterior displacement was correlated with superior [p = 0.002 (r=-0.697)] and inferior [p = 0.004 (r = 0.658)] oropharynx volume, suggesting that greater amounts of mandibular advancement are correlated to a decrease in the superior oropharynx and an increase in the inferior oropharynx. In the MMA group, the superior oropharynx volume was correlated to mandibular anteroposterior [p = 0.029 (r=-0.530)] and vertical displacement [p = 0.047 (r = 0.488)], indicating greater amounts of mandibular advancement may lead to a lowest gain in the superior oropharynx volume, while a great mandibular superior displacement is correlated with improvements in this region. CONCLUSIONS: The MAD therapy led to a clockwise mandibular rotation, increasing the dimensions of the superior oropharynx; while a counterclockwise rotation with greater increases in all UA regions were showed in the MMA treatment.

Three-dimensional decision support system for treatment of canine impaction.

INTRODUCTION: This study aimed to develop a 3-dimensional (3D) characterization of the severity of maxillary impacted canines and to test the clinical performance of this characterization as a treatment decision support tool. METHODS: Cone-beam computed tomography images obtained from 83 patients with 120 impacted maxillary canines were included. Quantitative information on the canine 3D position and qualitative assessment of root damage of adjacent teeth were evaluated. A severity index was constructed on the basis of the quantitative findings. Clinical applicability was tested by comparing clinical diagnosis and treatment planning for conventional records vs the 3D characterization via a 2-part survey. RESULTS: The average quantitative assessments of impacted maxillary canine position were 6.4 ± 3.6 mm from the midsagittal plane, 11.6 ± 3.1 mm in height relative to the occlusal plane, 31.5° ± 18° of roll, and 48.8° ± 14.3° of pitch. The severity index ranged from 0-13 with a mean score of 4.5 ± 2.2. Overlap with adjacent teeth was the greatest contributor (33%) to the index. Bicortically impacted canines caused the most severe root damage. Cone-beam computed tomography was preferred for assessing root damage and overall severity, whereas conventional imaging was sufficient for height and angulation assessment. The 3D report was very important or important for evaluating root damage, canine position, overall severity, and overlap. The 3D report changed most of the decisions relating to biomechanics, patient education, and treatment time estimate. The decision of exposure and traction vs extraction was changed 22% of the time after the presentation of the 3D report. CONCLUSIONS: The overlap with adjacent teeth frequently contributes the most to the severity index. The 3D report provided relevant clinical information regarding the canine position, damage to adjacent teeth, and the severity index, with a profound impact on the decisions of the clinicians regarding biomechanics, patient education, and treatment time estimate.

Automatic landmark identification in cone-beam computed tomography.

OBJECTIVE: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. MATERIALS AND METHODS: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position. RESULTS: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU. CONCLUSION: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.

DENTALMODELSEG: FULLY AUTOMATED SEGMENTATION OF UPPER AND LOWER 3D INTRA-ORAL SURFACES.

In this paper, we present a deep learning-based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as a UNET. We test our method in a dental application for the segmentation of dental crowns. The neural network is trained for multi-class segmentation, using image labels as ground truth. A 5-fold cross-validation was performed, and the segmentation task achieved an average Dice of 0.97, sensitivity of 0.98 and precision of 0.98. Our method and algorithms are available as a 3DSlicer extension.

Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.

Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3° and <2mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing, and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.

Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression.

In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information.

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.

Three-dimensional digital applications for implant space planning in orthodontics: A narrative review.

In the digital dentistry era, new tools, algorithms, data science approaches, and computer applications are available to researchers and clinicians. However, there is also a strong need for better knowledge and understanding of multisource data applications, including three-dimensional imaging information such as cone-beam computed tomography images and digital dental models for multidisciplinary cases. In addition, artificial intelligence models and automated clinical decision systems are rising. The clinician needs to plan the treatment based on state-of-the-art diagnosis for better and more personalized treatment. This article aimed to review basic concepts and the current panorama of digital implant planning in orthodontics, with open-source and closed-source tools for assessing cone-beam computed images and digital dental models. The visualization and processing of the three-dimensional data allow better implant planning based on bone conditions, adjacent teeth and root positions, and the prognosis of the case. We showed that many tools for assessment, segmentation, and visualization of cone-beam computed tomographic images and digital dental models could facilitate the treatment planning of patients needing implants or space closure. The tools and approaches presented are toward personalized treatment and better prognosis, following the path to a more automated clinical decision system based on multisource three-dimensional data, artificial intelligence models, and digital planning. In summary, the orthodontist needs to analyze each patient individually and use different software or tools that better fit their practice, allowing efficient treatment planning and satisfactory results with an adequate prognosis.

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models.

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-ß1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-ß1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.

Presurgical orthodontic decompensation with clear aligners.

INTRODUCTION: Orthodontists, surgeons, and patients have taken an interest in using clear aligners in combination with orthognathic surgery. This study aimed to evaluate the accuracy of tooth movements with clear aligners during presurgical orthodontics using novel 3-dimensional superimposition techniques. METHODS: The study sample consisted of 20 patients who have completed presurgical orthodontics using Invisalign clear aligners. Initial (pretreatment) digital dental models, presurgical digital dental models, and ClinCheck prediction models were obtained. Presurgical models were superimposed onto initial ones using stable anatomic landmarks; ClinCheck models were superimposed onto presurgical models using surface best-fit superimposition. Five hundred forty-five teeth were measured for 3 angular movements (buccolingual torque, mesiodistal tip, and rotation) and 4 linear movements (buccolingual, mesiodistal, vertical, and total scalar displacement). The predicted tooth movement was compared with the achieved amount for each movement and tooth, using both percentage accuracy and numerical difference. RESULTS: Average percentage accuracy (63.4% ± 11.5%) was higher than in previously reported literature. The most accurate tooth movements were buccal torque and mesial displacement compared with lingual torque and distal displacement, particularly for mandibular posterior teeth. Clinically significant inaccuracies were found for the buccal displacement of maxillary second molars, lingual displacement of all molars, intrusion of mandibular second molars, the distal tip of molars, second premolars, and mandibular first premolars, buccal torque of maxillary central and lateral incisors, and lingual torque of premolars and molars. CONCLUSIONS: Superimposition techniques used in this study lay the groundwork for future studies to analyze advanced clear aligner patients. Invisalign is a treatment modality that can be considered for presurgical orthodontics-tooth movements involved in arch leveling and decompensation are highly accurate when comparing the simulated and the clinically achieved movements.